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Can filter-feeding Asian carp invade the Laurentian Great Lakes?: a bioenergetic modelling
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exercise.
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Sandra L. Cooke,1,2,* Walter R. Hill1
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Illinois, 1816 South Oak Street, Champaign, Illinois 61820
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27708-0025
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Institute of Natural Resource Sustainability, Illinois Natural History Survey, University of
Present Address: Biology Department, Duke University, Box 90025, Durham, North Carolina
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Corresponding author: s.cooke@duke.edu, phone 919-660-7097, fax 919-681-0637
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Running head: Bioenergetics of invasive Asian carp
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Keywords: bighead carp, silver carp, invasive species, planktivore, risk assessment
Cooke and Hill
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Summary
1. There is much concern that filter-feeding Asian carp will invade the Laurentian Great
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Lakes and deplete crucial plankton resources. We developed bioenergetic models, using
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parameters from Asian carp and other fish species, to explore the possibility that
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planktonic food resources are insufficient to support the growth of silver carp
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(Hypophthalmichthys molitrix) and bighead carp (H. nobilis) in the Great Lakes.
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2. The models estimated basic metabolic requirements of silver and bighead carp under
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various body sizes, swimming speeds, and reproductive stages. These requirements were
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then compared to planktonic food resources and environmental temperatures to predict
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when and where silver and bighead carp may survive in the Great Lakes, and how far
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they may travel.
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3. Parameter values for respiration functions were experimentally derived in a coordinated
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study of silver and bighead carp, while consumption parameters were obtained from the
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literature on silver carp. Other model parameters lacking for Asian carp, such as those
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for egestion and excretion, were obtained from the literature on other fish species.
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4. We found that full-sized bighead carp required 61.0 kJ d-1 just to maintain their body
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mass at 20oC, approximately equivalent to feeding in a region with 255 g L-1
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macrozooplankton (dry) or 10.43 gL-1 chlorophyll a. Silver carp energy requirements
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were slightly higher.
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5. When applied to various habitats in the Great Lakes our results suggest that silver and
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bighead carp will be unable to colonize most open water regions because of limited
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plankton availability. However, in some circumstances, carp metabolism at lower
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temperatures may be low enough to permit positive growth even at very low rations.
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Positive growth is even more likely in productive embayments and wetlands, and the
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modelled swimming costs in some of these habitats suggest that carp could travel >1 km
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d-1 without losing biomass.
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6. The simulation (and firmly hypothetical) results from this modelling study suggest when
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and where Asian carp could become established in the Great Lakes. Given the potential
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consequences to Great Lakes ecosystems if these filter feeders do prove capable of
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establishing reproducing populations, efforts to keep Asian carp out of the Great Lakes
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must not be lessened. However, we do encourage the use of bioenergetic modelling in a
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holistic approach to assessing the risk of Asian carp invasion in the Great Lakes region.
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Introduction
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As humans continue to transport species and erode natural barriers to biological
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invasions, ecosystems throughout the world have born the consequences. The Laurentian Great
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Lakes have experienced cascading ecological effects from over 180 invasive species (Holeck et
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al., 2004), and most scientists and resource managers agree that it is critically important to
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prevent further species introductions to and from this region (Vander Zanden & Olden, 2008).
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Two aquatic invaders of particular concern in the Great Lakes basin (and in over 30 other
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countries around the world) are bighead carp (Hypophthalmichthys nobilis Richardson) and
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silver carp (H. molitrix Valenciennes), collectively known as Asian carp. These carp have
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invaded the Mississippi River Basin and are now found in waterways connected to the
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Laurentian Great Lakes (Chick & Pegg, 2001). Bighead and silver carp are fast-growing, high-
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volume filter-feeders with a generalist diet of both phytoplankton and zooplankton (Kolar et al.,
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2007). Studies on the mobility of silver and bighead carp show they can travel up to 64 km d-1,
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depending on river flow and other variables (DeGrandchamp, Garvey & Colombo, 2008), and
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thus they have the potential to enter and invade new regions rapidly.
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Recent studies suggest that Asian carp may adversely affect native planktivorous fishes
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including gizzard shad (Dorosoma cepedianum LeSueur) and bigmouth buffalo (Ictiobus
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cyprinellus Valenciennes) in the Illinois River (Irons et al., 2007). There is concern that Asian
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carp in the Illinois River will enter Lake Michigan via the Chicago Sanitary and Ship Canal and
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similarly affect native planktivores, both invertebrate and vertebrate, through competition for
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limited food resources. Other possible modes of introduction into the Great Lakes include bait-
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bucket transfer, movement from other U.S. and Canadian catchments, and release from live fish
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markets (Herborg et al., 2007; Keller & Lodge, 2007).
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Although earlier models of invasion risk predicted the invasion potential of silver carp in
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the Great Lakes to be low (Kolar & Lodge, 2002), more recent ecological niche models predict
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that most of the Great Lakes catchments (Chen, Wiley & Mcnyset, 2007), and the Great Lakes
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themselves (Herborg et al., 2007), offer suitable environments for bighead and silver carp to
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establish reproducing populations. These models use variables such as precipitation, river
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discharge, slope and percent tree cover to predict the potential range of invasive species. While
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such climatic and hydrological variables are important, biotic variables, including food
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availability and energetic constraints, can be used to predict a more realistic range of suitable
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environments. Bioenergetics models are useful tools that allow fisheries ecologists and resource
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managers to predict growth and food requirements of both native and exotic species in aquatic
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ecosystems. Developing bioenergetic models of bighead and silver carp would enable a
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comparison of their energy requirements to the energy available in the plankton of potential
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invasion sites, such as the Great Lakes, which are oligotrophic in most locations. An
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understanding of bighead and silver carp bioenergetics should also be valuable for managing
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their populations in ecosystems where they are already established. Food availability has been
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suggested as an influence on microhabitat selection of Asian carp in the Illinois River
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(DeGrandchamp, Garvey & Colombo, 2008).
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Despite the abundance of Asian carp in both their native Asian and non-native North
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American habitats, the bioenergetic requirements of these fishes have only been sparsely
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reported in the mainstream literature (but see Mukhamedova, 1977). Our purpose was to
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develop bioenergetics models for bighead and silver carp using empirically-determined
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respiration rates and bioenergetic parameters derived from literature on Asian carp and other fish
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species. We used these models to assess the theoretical potential of bighead and silver carp to
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colonize habitats in the Laurentian Great Lakes, based on plankton biomass and surface water
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temperature data. Our theoretical analysis also takes into account the possible effects of thermal
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stratification and swimming costs on carp growth and movement estimates. We hypothesized
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that most open-water habitats of the Great Lakes will be much less likely than littoral habitats to
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support the establishment of bighead and silver carp, due to between-habitat differences in
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plankton biomass and temperature.
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Methods
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Model development
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We structured bighead carp and silver carp models following the widely-used Wisconsin
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bioenergetics model (Hanson et al., 1997). The basic energy balance equation described by the
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model is:
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C  ( R  A  SDA)  ( F  U )  (B  G )
(1)
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where total consumption of energy (C) is equal to the sum of metabolism (respiration, R; active
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metabolism, A; and specific dynamic action, SDA), wastes (faecal egestion, F; and urinary
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excretion, U), and growth (somatic growth, B; and gonad production, G). Each component of
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this overall equation has different forms of the temperature- and mass-dependence functions that
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can be used, depending on the physiology of each species.
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We used the form of the consumption function designed for warm-water species
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(equation 2 in Fish Bioenergetics 3.0 software; Kitchell, Stewart & Weininger, 1977; Hanson et
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al., 1997):
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C  CA  W CB  p  V x  e ( X (1V ))
(2)
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where C is the specific consumption rate (g g-1 d-1); CA and CB are the intercept and slope of the
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allometric mass function, respectively; W is fish mass (g); and p is the proportion of maximum
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consumption. The temperature dependence parameters are defined as follows:
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V  (CTM  T ) /(CTM  CTO)
(2a)
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X  (Z 2  (1  (1  40 / Y ) 0.5 ) 2 ) / 400
(2b)
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Z  Ln(CQ)  (CTM  CTO)
(2c)
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Y  Ln(CQ)  (CTM  CTO  2)
(2d)
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where T is temperature (oC), CQ is the temperature dependent coefficient, CTM is the maximum
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temperature above which consumption ceases, and CTO is the optimum temperature.
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Bighead and silver carp filtration and consumption rates are highly variable, being
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dependent not just on body size and temperature, but also on particle size (Kolar et al., 2007).
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Smith (1989) observed that juvenile silver carp had maximum filtration rates of particles >70 m
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compared to other particle sizes. Smith (1989) developed an allometric relationship that
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explained 99% of the variance for silver carp feeding at 20oC on particles >70 m, and we use
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these parameters for our bighead and silver carp consumption equation (Table 1). For the
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temperature dependence parameters of the consumption equation, we calculated mean CTO and
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CTM from values reported for Asian carp in the literature (reviewed by Kolar et al., 2007). We
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used CQ from tilapia (Sarotherodon spp. and Oreochromis spp.; Hanson et al., 1997) because
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tilapia are planktivorous filter-feeders with thermal tolerances and feeding characteristics very
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similar to Asian carp (Table 1). We also assumed that Asian carp energy density was the same
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as adult tilapia (5442 J g-1 wet mass; Hanson et al., 1997).
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In a review of fish bioenergetics studies, Chipps & Wahl (2008) discuss the importance
of not overestimating waste losses when modelling consumption at low rations. Thus, for
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modelling egestion (F) and excretion (U) we used equation set 2 in Fish Bioenergetics, which
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addresses this overestimation problem by accounting for fish mass, temperature and ration
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(Hanson et al., 1997):
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F  FA  T FB  e FG p  C
(3)
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U  UA  T UB  eUG p  (C  F )
(4)
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where FA and UA are intercepts of the proportion of consumed energy egested and excreted,
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respectively, versus temperature and ration; FB and UB are the coefficients of temperature
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dependence; FG and UG are the coefficients for ration dependence; p is ration (proportion of
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maximum consumption); and T is temperature. For these functions we used parameters modified
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for brown trout Salmo trutta Linnaeus (Hanson et al., 1997; Table 1). This set of parameters has
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been used for other species, including yellow perch Perca flavescens Mitchill (Kitchell, Stewart
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& Weininger, 1977) and lake trout Salvelinus namaycush Walbaum (Stewart et al., 1983).
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We used the form of the respiration function that allows an input of swimming speed
(equation 1 in Fish Bioenergetics, Hanson et al., 1997):
R  RA  W RB  e RQT  e RTOVEL
(5)
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where R is the specific rate of respiration (g g-1 d-1); RA and RB are the intercept and slope of the
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allometric mass function, respectively; RQ approximates the Q10; RTO is swimming speed in cm
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s-1; and VEL is a function that allows swimming speed to vary with body mass and temperature.
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When using Fish Bioenergetics 3.0 software, if swimming speed is a constant (i.e. not dependent
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on mass or temperature) then the activity multiplier is set to 1 and RTO is set to the desired
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velocity. To develop our model, we assumed that average swimming speed was constant
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because the swimming respirometer allowed maintenance of a more or less constant swimming
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velocity for each fish (see below). We also assumed that specific dynamic action (SDA) for both
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species was equal to 0.10 (Table 1), the value developed for tilapia (Hanson et al., 1997).
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Respiration rates of adult and juvenile bighead and silver carp were taken from Hogue &
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Pegg (2009), who measured oxygen consumption with both a static respirometer and a Brett-
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style swimming respirometer following the methods of Cech (1990). Respiration in the Hogue
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& Pegg (2009) study was measured over a temperature range of 4.5 oC to 26.9oC. Because those
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authors had difficulty in getting bighead carp to swim in the swimming respirometer, only
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resting data were used for bighead carp, whereas the silver carp trials were successfully
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conducted at 0, 20, and 30 cm s-1.
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To obtain values for RA, RB and RQ for silver carp we used a Ln-transformed version of
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Eq. 5, so that the respiration data could be fitted to the equation using multiple linear regression
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(Stewart et al., 1983)
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ln R  RA  RB ln( W )  RQT  vU
(5a)
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where ν is an empirical constant and U is swimming speed in cm s-1. The parameters we
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obtained were:
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ln R  5.88  0.239 ln( W )  0.076T  0.0074U
(5b)
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The model fitted the data reasonably well (adjusted R2 = 0.77), and all parameters were
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significant (P < 0.0001), with the exception of ν (P = 0.12) whose lack of significance may have
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resulted from relatively few observations at different swimming speeds. Thus, the form of the
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respiration equation we used for silver carp in Fish Bioenergetics 3.0 was:
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R  0.00279  W 0.239  e 0.076T
(5c)
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For bighead carp, multiple linear regression of the respiration data was used to fit values
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for RA, RB and RQ similar to those for silver carp except that a swimming component was not
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included. The resulting equation was:
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R  0.00528  W 0.299  e 0.048T
(5d)
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This model fitted the data reasonably well (adjusted R2 = 0.74) and all parameters were
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significant (P < 0.0002).
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Sensitivity analysis
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We conducted a sensitivity analysis on the parameters listed in Table 1 following the
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procedure in Kitchell, Stewart & Weininger (1977) and Stewart et al. (1983). Each parameter
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was separately varied by +10% and -10% from its nominal value and the resulting output
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(specific consumption rate required for metabolic maintenance) was compared to the nominal
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specific consumption rate (from the standard simulation). This sensitivity analysis was done for
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a 2400 g resting adult of each species feeding on zooplankton prey with an energy density of
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2512 J g-1 wet mass (Cummins & Wuycheck, 1971) at 20oC. Sensitivities were calculated as
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follows:
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s( p) 
10  C
C
(6)
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where s(p) is sensitivity of parameter p (a value of 1 means that a 10% change in the parameter
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causes a 10% change in consumption), C is the simulated required daily consumption rate for
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metabolic maintenance, and C is the change in C due to the change in p. Parameters that were
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most sensitive include RA and RB, but no parameter had a sensitivity value larger than 2.58
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(Table 2), and all sensitivity parameters were within the range of those observed in other
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bioenergetics studies (e.g. Kitchell,Stewart & Weininger, 1977; Stewart et al., 1983).
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Model application
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Fish Bioenergetics 3.0 software, which was developed for the Wisconsin model (Hanson
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et al., 1997), was used to predict daily consumption requirements of bighead and silver carp for
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basic metabolic maintenance (no growth). We generated simulations for three different body
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sizes: 10 cm long (10 g) juveniles; 20 cm long (70 g) subadults; and 60 cm long (2400 g) adults.
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Consumption requirements were generated for different swimming speeds (0-4 cm s-1) at 20oC
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(we kept temperature constant here because our purpose was to assess the effects of body size
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and swimming speed on consumption). These swimming speeds may seem low, but when
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calculated as daily distance travelled (e.g. 4 cm s-1 is 3.5 km d-1) they fall within the range of
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observed mean movement rates for both species (0.21-10.61 km d-1; DeGrandchamp, Garvey &
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Colombo, 2008). Consumption requirements were also estimated for a 2400 g female of each
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species spawning 5% of its body mass, or a gonadosomatic index (IG ) of 5. Papoulias, Chapman
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& Tillett (2006) report IG values of 0-9.6 for bighead and silver carp in the Missouri River.
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Simulation outputs were obtained as specific consumption rates (J g-1 d-1) required for metabolic
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maintenance (no growth) for resting and swimming fish. For each size fish, the consumption
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rate in kJ d-1 was converted to kJ L-1 by taking into account the filtration rate of the fish. Using
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the allometric relationship from Smith (1989) we assumed that 10 g, 70 g and 2400 g Asian carp
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filter 191, 764 and 9502 L d-1, respectively. In order to express these energetic requirements in
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terms of environmental prey densities, we converted the kJ L-1 values to chlorophyll a (Chl a)
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concentrations and zooplankton dry biomass (both in g L-1). For zooplankton, we assumed that
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all prey had a mean energy density of 2512 J g-1 wet mass (Cummins & Wuycheck, 1971) and
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that the ratio of zooplankton wet mass to dry mass was 10:1 (Dumont, Van de Velde & Dumont,
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1975). To obtain Chl a concentrations we assumed that all phytoplankton had a mean energy
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density of 2460 J g-1 wet mass (Hambright, Blumenshine & Shapiro, 2002), the ratio of wet mass
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to dry mass was 2.5 (Reynolds, 1984), and that the ratio of Chl a to dry mass was 100 (Reynolds,
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1984).
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To model the growth of carp feeding in different types of habitats within the five
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Laurentian Great Lakes we compiled data on Chl a, phytoplankton biomass, zooplankton
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densities, zooplankton biomass and water temperature from multiple regions. In cases where
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surface water temperatures were not provided with the plankton data, we approximated the
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“typical” temperature for a site and season based on the temperature data available from
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NOAA’s Great Lakes Environmental Research Laboratory (http://www.glerl.noaa.gov/data/) and
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the EPA’s Great Lakes Environmental Database
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(http://www.epa.gov/greatlakes/monitoring/data_proj/glenda/index.html). Our goal was to
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include a selection of recently sampled sites representative of offshore pelagic habitats as well as
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coastal embayments and wetlands with higher plankton biomass. We also included plankton
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data collected at different times of year across a range of temperatures. This selection of
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locations and seasons is far from comprehensive, but nevertheless represents a broad range of
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habitats within the Great Lakes. The data selected include a recent study of a late winter
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production pulse in southern Lake Michigan (Kerfoot et al., 2008); a survey of Great Lakes
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wetlands (Lougheed & Chow-Fraser, 2002; open water habitats only); and a comparison of
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zooplankton biomass and Chl a in embayment, nearshore and offshore regions in Lake Ontario
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(Hall et al., 2003). We also applied the model to several riverine sites to demonstrate that the
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model predicts positive growth in habitats where Asian carp have already invaded. We used the
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bioenergetics models to estimate expected growth (biomass loss or gain) of juvenile (10 g) and
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adult (2400 g) non-swimming bighead and silver carp in each region over 30 days (a reasonable
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amount of time to allow a potential new invader to establish a “foothold” in the new habitat).
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For habitats in which non-swimming Asian carp were predicted to have positive growth we
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determined the maximum distance that the carp could travel without losing biomass by setting
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growth to zero (metabolic maintenance), solving the bioenergetics model for mean velocity
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(RTO in Fish Bioenergetics 3.0), and then calculating distance travelled over 30 days based on
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this mean velocity. These distance calculations are merely estimates that assume that water flow
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is not a factor.
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In generating estimates of growth and swimming costs and we used the filtration rates
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and prey energy density stated previously. Most of the zooplankton data available were densities
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rather than biomass. To convert zooplankton densities to dry mass we assumed that each taxon
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exhibited average body lengths and applied length-weight regressions from Culver et al. (1985),
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which were developed for Great Lakes zooplankton. For rotifers, we used the mean biomasses
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presented in Dumont, Van de Velde & Dumont (1975). Not all zooplankton data sets included
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rotifers, and we noted these exceptions. Because most of the phytoplankton data were in Chl a
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concentrations rather than phytoplankton biomass, we used the conversion factors mentioned
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previously.
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To assess the effects of temperature on growth rates we selected four open water habitats
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that would normally be thermally stratified during the summer, for which summertime plankton
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data are available, and for which modelled growth at surface temperature was negative. These
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sites were Lake Michigan’s southern basin (September), Collingwood Harbour of Lake Huron,
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the central basin of Lake Erie, and the nearshore zone of Sandy Pond (Lake Ontario). We simply
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re-ran the model at different temperatures from 4-24oC for 30 days of feeding. We ran separate
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simulations at a constant temperature for each temperature in order to compare more effectively
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the growth of a carp feeding at the chlorophyll maximum and at other depths (with cool water)
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with carp growth in the warmer epilimnion.
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Measured respiration rates of any fish species may overestimate resting metabolic
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requirements because of the difficulty in measuring the respiration of completely inactive fish in
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a chamber (Cech, 1990; Hogue & Pegg, 2009). To counter the potential issue of overestimating
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energy requirements and underestimating growth, we made several conservative assumptions. In
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addition to the assumption of all particle sizes being consumed at the same maximum rate, we
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assumed that all prey were 100% digestible; all prey were easily captured by passive filter-
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feeding (no escape by swimming zooplankton); and all prey had a constant, relatively high
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energy density. We also assumed that carp filtration rates did not vary with swimming speed
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(when in reality a swimming fish would filter a higher volume than a resting fish). However,
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because we may have overestimated resting filtration rates and because the simulated swimming
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speeds were so low, the differences in filtration rates due to swimming speed are probably
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negligible.
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Results
The specific consumption rate required to maintain a constant fish mass increased with
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swimming speed and decreased with fish size (Fig. 1). At 20oC silver carp specific consumption
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rates were slightly higher than those of bighead carp: resting silver carp weighing 10 g, 70 g and
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2400 g required 1.4, 6.1 and 91 kJ d-1, respectively, while bighead carp of those masses required
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1.3, 5.1 and 61 kJ d-1. A 2400 g reproducing silver carp (IG = 5) requires 415 kJ d-1, while a
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similar bighead required 380 kJ d-1. Consumption requirements of reproducing carp were higher
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at all swimming speeds, although the differences between reproducing and non-reproducing
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adults were smaller at higher speeds because of the higher energetic costs of swimming
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compared to reproducing (Fig. 1). When the daily requirements and Asian carp filtration rates
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are translated into Chl a currency, resting silver carp weighing 10, 70 and 2400 g require 11.91,
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13.08 and 15.50 g L-1 Chl a to maintain their mass (Fig. 2). When compared as environmental
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densities of zooplankton, resting silver carp of those masses require 292, 320 and 379 g L-1 of
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zooplankton dry mass, and bighead carp require 273, 266 and 255 g L-1dry mass (Fig. 2).
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Environmental requirements were similar for non-reproducing Asian carp of different sizes
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because of the greater filtration rates of larger carp. The model assumed that activity was an
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exponential function of swimming speed, and thus predicted specific consumption rates of
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swimming carp were quite high compared to resting carp (e.g. Fig. 1).
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The projected growth of non-swimming silver and bighead carp feeding on
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phytoplankton and zooplankton in different regions of the Great Lakes was negative in almost all
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open water regions of Lakes Michigan, Superior, Huron and Ontario (Table 3). However,
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positive growth was predicted in Green Bay, western Lake Erie and all of Lake Erie during the
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spring, the embayment regions of Sodus Bay and Sandy Pond (Lake Ontario), and some
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wetlands. Modelled growth was positive in the riverine habitats where Asian carp have
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established reproducing populations (Table 3).
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The maximum distance that carp could travel in different Great Lakes habitats without
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losing biomass over 30 days ranged from 0.8 to 33.7 km for 10 g bighead and silver carp and 2.4
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to 35.0 km for 2400 g carp (Table 4). The range of maximum movement predicted in riverine
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habitats ranged from 9.3 to 38.9 km for 10 g carp and 2.6 to 39.9 km for 2400 g carp (Table 4).
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Growth simulations at different temperatures suggest that carp would generally have
higher growth rates at lower temperatures (Fig. 3). Although carp were projected to lose
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biomass at surface temperatures, modelled growth was positive at low temperatures (<8oC) in all
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four habitats.
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Discussion
Our modelling results indicate that the low concentrations of plankton in many open
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water regions of the Laurentian Great Lakes cannot support growth of silver and bighead carp.
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The threat of these filter-feeding fish establishing open water populations and disrupting the
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pelagic food web in the oligotrophic regions of the lakes therefore appears to be small.
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However, our results also indicate that more productive regions, such as Green Bay, the western
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basin of Lake Erie and some other embayments and wetlands, may contain enough plankton to
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meet energetic requirements at certain times of year and at certain temperatures. Furthermore,
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although modelled energetic costs of swimming were high, the simulations predict that carp in
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some of these habitats could still travel up to 40 km over a 30 day period while maintaining body
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mass. Many embayments, wetlands and other coastal zones are important habitats and nurseries
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for larval fishes such as alewives (Alosa pseudoharengus Wilson; Klumb et al., 2003) and
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walleye (Sander vitreus Mitchill; Roseman et al., 2005). Thus, although Asian carp would
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probably not become established in most nearshore or offshore pelagic habitats of Lakes Ontario,
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Michigan, Superior and Huron, they might indirectly affect those ecosystems if they became
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established in adjoining embayments and wetlands. The effect of Asian carp on native fishes
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may be similar to that of zebra mussels, whose invasion in Lake Michigan altered the foraging
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patterns of alewives and other species despite having only partial spatial and diet overlap
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(Pothoven & Madenjian, 2008).
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Bighead and silver carp mostly inhabit warm, shallow water bodies such as rivers and
364
backwater lakes, and to our knowledge there is little research indicating at what depth in a
365
thermally stratified pelagic zone Asian carp would prefer feeding. They are not visual feeders,
366
and so temperature and plankton availability, rather than light, are likely to determine depth
367
preference. The temperatures of maximum consumption for bighead and silver carp are 26 and
368
29oC, respectively, but at low rations maximum growth occurs at lower temperatures, according
369
to the bioenergetic models. This is consistent with bioenergetic simulations by Kitchell, Stewart
370
& Weininger (1977), in which the maximum growth rate of 10 g perch occurs around 4oC when
371
only feeding at 3% of their body weight per day, but the CTO is 23oC. Although carp growth
372
was predicted to be negative in the four sites shown in Fig. 3, when modelled at surface water
373
temperatures (20-22.5oC), growth could be positive if the carp were to feed in deeper and cooler
374
water where zooplankton tend to reside during the day.
375
It should be noted that plankton densities and other ecological conditions in the Great
376
Lakes can change due to climate, land use, additional invasive species and other factors.
377
Habitats invaded by filter-feeding zebra mussels might be less conducive to an Asian carp
378
invasion, but it is unknown if the veliger larvae could serve as a substantial food source for carp.
379
Also, selective herbivory by zebra mussels promotes cyanobacteria blooms in some habitats (e.g.
380
Pillsbury et al., 2002), which could benefit carp. Changing temperature and increased nutrient
381
inputs could increase plankton biomass or alter zooplankton and phytoplankton community
382
structure, which could in turn increase the potential for carp growth in the Great Lakes. Our
383
analysis provides some insight into the range of feeding conditions and temperatures that could
384
facilitate Asian carp invasion should conditions change.
17
Cooke and Hill
385
We emphasize that this study is only a first attempt at a model for these ecologically
386
important invasive species. The model’s conclusions are only as robust as the assumptions and
387
parameters that are part of it. For this exercise, we were forced to borrow some of the
388
bioenergetics parameters from studies on other species. While such sharing of parameters is
389
widespread in the fish bioenergetics literature (e.g. Chipps & Wahl, 2004; Petersen & Paukert,
390
2005) we acknowledge that further laboratory evaluations of the parameters could improve the
391
performance of the model and the accuracy of its predictions. For example, we assumed that
392
carp had a constant energy density across body sizes, when in reality larger fish would have
393
higher energy densities than smaller fish. Thus, the 5442 J g-1 wet mass value we used from
394
adult tilapia may be too low for large, 2400 g adult Asian carp, and may be too high for sub-
395
adults and juveniles. If this is the case, then adult carp energetic requirements would be even
396
higher than we predicted, and juvenile carp requirements would be lower. Additionally,
397
although we used experimentally-derived consumption rates from a study of silver carp (Smith,
398
1989), we assumed that all particle sizes (phytoplankton and zooplankton) could be filtered at the
399
same maximum rate, despite evidence that carp filter smaller particles less efficiently (Smith,
400
1989), and we assumed bighead and silver carp have similar consumption rates. The apparent
401
absence of allometry for the energetic requirements of bighead carp (Fig. 2b) may indicate that
402
the filtration rates taken from Smith (1989) were overestimates for sub-adult and adult bighead
403
carp. Caged bigheads ranging in weight from 52 g to 139 g had a mean filtration rate of 4.44 L
404
hour-1 g-1 when feeding on rotifers (Opuszynski, Shireman & Cichra, 1991), which is equal to a
405
filtration rate of 311 L d-1 for a 70 g fish. But we assumed a 70 g carp could filter 764 L d-1.
406
Chipps & Wahl (2008) have observed that model-predicted consumption rates are often higher
407
than those observed in the field. The consequence of making such assumptions and
18
Cooke and Hill
408
overestimating filtration rates is that our conclusions with respect to carp growth are
409
conservative and more likely to predict higher growth rates than might actually occur.
410
A strength of our modelling effort was that we used allometric- and temperature-
411
dependent respiration parameters for both bighead and silver carp that were determined in a
412
coordinated study (Hogue & Pegg, 2009). Although the juvenile and adult life stages of some
413
fish have different allometric curves for metabolism (Post, 1990), we combined the adult and
414
juvenile data into one model because Hogue & Pegg (2009) found that life stage did not affect
415
mass-independent oxygen consumption rates. The sensitivities of the respiration parameters
416
were higher than those of most other parameters, but they were comparable to other published
417
sensitivity values for metabolism (Stewart et al., 1983). However, the confidence intervals for
418
the respiration parameters were large due to the low sample size. Many more respiration trials
419
were conducted, but we only used those values for which we were confident that the carp were
420
swimming normally (or exhibiting minimal swimming in the resting trials).
421
In modelling the expected growth of Asian carp in different habitats we assumed these
422
fishes were feeding on zooplankton and phytoplankton only, as most of the published literature
423
on Asian carp feeding has focussed on these two primary food sources (reviewed by Kolar et al.,
424
2007). In some cases where plankton availability is low, both carp species have been reported to
425
consume large quantities of detritus (Opuszynski, 1981). It is not uncommon for particulate
426
organic carbon (POC), rather than algae, to comprise the majority of seston carbon in rivers
427
(Acharya et al., 2006). Thus it is likely that Asian carp in rivers with low plankton availability,
428
such as portions of the Missouri River, may depend more on POC than in other habitats. The
429
nutritional quality of this food source is often low for zooplankton such as Bosmina (Acharya et
430
al., 2006), so we suspect that POC is likely to be a poor food for Asian carp as well. Also, it is
19
Cooke and Hill
431
unknown if Asian carp would feed in lake benthic zones where most POC and detritus is found
432
or consume benthic macroinvertebrates. Bighead carp may be able to consume these larger
433
organisms, but it is suggested that a small foregut prevents silver carp from consuming large
434
food items (Kolar et al., 2007). Other potential food sources not considered in these models are
435
protozoans and other microplankton. Carrick (2005) observed that heterotrophic protozoan
436
biomass is equivalent to at least 70% of crustacean biomass in Lake Michigan, and Munawar &
437
Lynn (2002) observed that mean ciliate biomass ranges from <1 to >680 mg m-3 in Lakes
438
Superior, Huron, Erie and Ontario. While even the higher end of this range may be insignificant
439
compared to the biomass requirements of Asian carp, a refined bioenergetic analysis that
440
includes these prey sources may be warranted in certain ecosystems. Another important point is
441
that the plankton densities in Table 3 are regional whole water column averages that do not take
442
into account possible zooplankton swarms and patchiness. In the Great Lakes, extreme wind,
443
gyres and other physical phenomena may create patches of zooplankton sufficiently dense to
444
support Asian carp, although such patches are probably temporary and may not be able to sustain
445
lasting populations.
446
Rotifer data were not available for all plankton datasets with which we applied the
447
models (Table 3). A recent study in the Mississippi and Illinois rivers found that the diets of
448
bighead and silver carp were dominated by rotifers rather than crustaceans (Sampson, Chick &
449
Pegg, 2009). This is at least partially due to the fact that rotifers were more abundant than
450
crustacean zooplankton in these habitats, suggesting that the carp modified their diet based on
451
prey availability, as other studies have shown (Kolar et al., 2007). Rotifers generally make up a
452
small percentage (~5-20%) of zooplankton biomass in the Great Lakes (Barbiero & Tuchman,
453
2002), especially compared to some rivers (Sampson, Chick & Pegg, 2009), but the absence of
20
Cooke and Hill
454
rotifer data may slightly underestimate potential growth of carp in some Great Lakes habitats.
455
Potential growth may also be underestimated in the Middle Mississippi River where rotifer data
456
were unavailable, although modelled growth was positive in all riverine habitats that we
457
examined – habitats where Asian carp currently thrive (Table 3).
458
Despite the preceding caveats, some important implications emerge from the
459
bioenergetics modelling. Recent spatially comprehensive studies show that low plankton
460
biomass is prevalent in both nearshore and offshore regions of Lake Michigan (Vanderploeg et
461
al., 2007). If Asian carp were to enter the ‘plankton desert’ of Lake Michigan via the Chicago
462
Sanitary and Ship Canal (CSSC), it seems unlikely (but not impossible) that they would be able
463
to derive enough energy from the plankton to support the energetic costs of travelling to Green
464
Bay or another ‘plankton oasis’. Our analysis suggests that a greater Asian carp invasion risk
465
may be posed by the inadvertent use of Asian carp as bait or by Canadian live fish markets in
466
close proximity to productive harbours and embayments of Lakes Ontario and Erie (Herborg et
467
al., 2007; Keller & Lodge, 2007). However, we do not advocate removal of the existing electric
468
fish barrier in the CSSC to prevent migration to and from Lake Michigan, as the modelling
469
results suggest that positive growth is indeed possible in this region at cooler temperatures
470
despite the low plankton rations (Fig. 3a).
471
Bioenergetics models are only one component of what should be a more extensive
472
approach to assessing the invasion risk for Asian carp. We advocate testing model predictions
473
empirically, under as realistic conditions as possible. A recent laboratory experiment showed
474
negative growth of bighead carp feeding at mesotrophic plankton densities and positive growth
475
at eutrophic values (Cooke, Hill & Meyer, 2009); however, the plankton composition in this
476
experiment (primarily Daphnia magna Straus and Microcystis spp.) did not resemble that of
21
Cooke and Hill
477
most Great Lakes habitats. Lakeside growth studies, in which Lake Michigan water is fed to
478
Asian carp enclosed in mesocosms, would be preferable in testing our hypotheses that plankton
479
resources in the Great Lakes are too sparse to support the growth of filter-feeding carp. Hitherto,
480
government agencies have been reluctant to approve lakeside studies because of concern over the
481
potential escape of Asian carp from the mesocosms. Appropriate design features could be
482
implemented to eliminate the potential for escape, however, and given the current public concern
483
about the invasion threat posed by the fish, additional information gained through empirical
484
testing should be welcomed by both environmental managers and politicians.
485
Further, while bioenergetic predictions may provide some information on the potential
486
for Asian carp to grow and reproduce in new habitats, recent research highlights the importance
487
of river discharge and temperature in influencing the spawning success and larval recruitment of
488
bighead and silver carp (DeGrandchamp, Garvey & Csoboth, 2007; Lohmeyer & Garvey, 2009).
489
Data from the Upper Mississippi River System suggest that the reproductive potential of both
490
species is reduced in slow-flowing water (DeGrandchamp, Garvey & Csoboth, 2007; Lohmeyer
491
& Garvey, 2009). However, spawning in low-flow conditions has been observed in isolated
492
cases (Kolar et al., 2007), and more research is needed on Asian carp spawning and recruitment.
493
In conclusion, our results suggest that silver and bighead carp will be unable to colonize
494
many open water regions because of limited plankton availability. However, our results also
495
suggest that in some habitats plankton resources are sufficient to support positive growth of
496
bighead and silver carp, even when taking into account high swimming costs. Furthermore, carp
497
metabolism in cool water is low enough to support positive growth at very low rations in some
498
habitats. Because the probability of a successful invasion increases as more Asian carp
499
individuals are introduced into the Great Lakes, current efforts to prevent introductions should at
22
Cooke and Hill
500
least be maintained if not expanded. More broadly, we recommend that aquatic resource
501
managers in other locations threatened by bighead and silver carp incorporate a bioenergetics
502
approach into more holistic invasion risk assessments.
503
504
Acknowledgments
505
We thank the following people who provided unpublished data or further information on
506
their published datasets: Paul Bukaveckas; Carla Cáceres; John Chick and Alexander Levchuk;
507
Kelli Dickerson and John Havel; Richard Fulford; Spencer Hall; and Vanessa Lougheed and
508
Patricia Chow-Fraser. We thank Jennifer Hogue for providing respiration data, Greg Sass and
509
the staff at the Illinois River Biological Station for providing facilities, Kevin Meyer for
510
assistance with respiration experiments, and James Garvey and two anonymous reviewers for
511
feedback that improved this paper. This work was supported by an Aquatic Invasive Species
512
grant from the National Sea Grant Office of the National Atmospheric and Oceanic
513
Administration.
514
515
516
517
518
519
520
521
522
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523
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Table 1. Bioenergetics parameters for silver (first value) and bighead carp (second value). In
some cases the same parameter was used for both species.
Parameter
Description
Value
Consumption
CA
Intercept for maximum consumption
CB
Mass dependence coefficient
CQ
Temperature dependence coefficient
1.54a
-0.287a
2.5b
CTO
Optimum temperature (oC)
29, 26c
CTM
Maximum lethal temperature (oC)
43, 38c
Egestion and Excretion
FA
Intercept of the proportion of consumed energy egested
0.212d
FB
Temperature dependence coefficient for egestion
-0.222d
FG
Ration dependence coefficient for egestion
0.631d
UA
Intercept of the proportion of consumed energy excreted
0.031d
UB
Temperature dependence coefficient for excretion
0.58d
UG
Ration dependence coefficient for excretion
-0.299d
Metabolism
RA
Intercept of mass dependence function
0.0028, 0.0053
RB
Slope of mass dependence function
-0.239, -0.299
RQ
Approximates Q10 over low temperatures
0.076, 0.048
ACT
Activity multiplier for a constant swimming speed
1.0
SDA
Proportion of assimilated energy lost to specific dynamic action
0.1b
a
Smith, 1989
b
Nitithamyong (in Hanson et al., 1997)
c
Kolar et al., 2007
d
Elliott, 1976 (in Hanson et al., 1997)
31
Cooke and Hill
Table 2. Sensitivities of the specific consumption rate required for routine metabolic
maintenance (no growth) to deviations of each input parameter. Sensitivities were calculated for
2400 g resting silver carp and bighead carp at 20oC.
Input error – silver
Input error – bighead
Parameter
+10%
-10%
+10%
-10%
Consumption
CA
CB
+0.00
-0.00
+0.00
+0.01
-0.01
-0.01
+0.00
-0.00
+0.01
-0.00
-0.00
-0.00
CTO
CTM
Egestion
FA
FB
FG
Excretion
UA
+0.02
-0.00
-0.00
+0.01
-0.01
-0.01
-0.01
+0.00
+0.14
+0.10
+0.02
-0.13
-0.08
-0.02
+0.12
+0.09
+0.00
-0.12
-0.09
+0.00
+0.21
-0.25
+0.21
-0.26
UB
UG
Metabolism
RA
RB
RQ
SDA
+0.46
+0.02
-0.37
-0.02
+0.43
-0.03
-0.40
-0.03
+1.84
+2.05
+1.65
+0.14
-0.32
-1.70
-1.42
-0.14
+0.96
+2.58
+0.96
+0.11
-1.04
-2.10
-0.94
-0.17
CQ
32
Table 3. Projected growth, based on bioenergetics models, of juvenile (10 cm, 10 g) and adult (60 cm, 2400 g) non-swimming
bighead carp (BC) and silver carp (SC) foraging on zooplankton (zoop) and phytoplankton (phyto) for 30 days at different times of
year and in different regions of Lakes Michigan, Superior, Huron, Erie and Ontario, and riverine habitats currently invaded by Asian
carp (Spr = spring; Sum = summer). Open water habitats near wetlands are indicated after the site name (wet), zooplankton samples
excluding rotifers are noted in the reference column (NR), and negative growth values are highlighted in boldface.
Cooke and Hill
Time of year,
water
temperature
(oC)
Phyto. wet
mass
(mg L-1)
Zoop. wet
mass
(mg L-1)
10 g BC
10 g SC
2400 g BC
2400 g SC
May, 8.8
0.69
0.024
-15
-9
-3
-3
Jul, 19.4
0.69
0.15
-29
-30
-6
-10
Sep, 20.2
0.69
0.79
-20
-23
-4
-9
within production pulse
Apr, 3.4
0.52
0.18
-9
-2
-2
-1
outside production pulse
Apr, 3.6
0.19
0.045
-17
-11
-4
-3
Green Bay
Apr, 13.4
5.5
0.16
+63
+66
+15
+13
Jun, 20.3
5.5
4.72
+120
+113
+28
+23
May, 3.4
0.31
0.16
-13
-6
-3
-2
Aug , 9.4
0.32
0.59
-12
-6
-2
-3
Chippewa Park (wet)
Jul, 22.5
1.42
2.52
+13
+5
+4
-3
Pine Bay (wet)
Jul, 22.5
1.13
2.22
+3
-4
+2
-5
Hurkett Cove (wet)
Jul, 22.5
0.07
3.19
+2
-5
+1
-5
Collingwood Harbour
(Georgian Bay)
Jul, 22.5
1.42
1.83
+2
-5
+1
-5
Oliphant Bay (wet)
Jul, 22.5
0.57
0.056
-38
-43
-9
-15
Baie du Dore (wet)
Jul, 22.5
0.07
0.024
-46
-51
-10
-16
Location
Predicted % biomass gain or loss over 30 days
References
Lake Michigan
southern basin, nearshore
C. E. Cáceres unpubl.
data (zoop); Gardner et
al., 2004 (Chl a)
Kerfoot et al., 2008
Fulford et al., 2006, pers.
comm. (zoop, NR);
Qualls et al., 2007 (Chl
a)
Lake Superior
western arm
Brown & Branstrator,
2004 (NR)
Lougheed & ChowFraser, 2002, pers.
comm.
Lake Huron
34
Lougheed & ChowFraser, 2002, pers.
comm.
Cooke and Hill
Table 3, continued
Time of year,
water
temperature
(oC)
Phyto. wet
mass
(mg L-1)
Zoop. wet
mass
(mg L-1)
10 g BC
10 g SC
2400 g BC
2400 g SC
Spr, 6.1
3.31
0.69
+46
+50
+10
+9
Sum, 21.0
3.12
1.74
+31
+25
+8
+2
Spr, 6.1
0.63
1.14
+8
+14
+2
+2
Sum, 21.0
1.21
0.95
-12
-16
-2
-7
Spr, 6.1
1.82
0.29
+13
+20
+3
+3
Sum, 21.0
1.06
0.62
-20
-23
-2
-9
Rondeau Prov. Park (wet)
Jun, 22.5
0.14
0.46
-38
-43
-9
-15
Long Point Prov. Park (wet)
Jul, 22.5
0.36
0.40
-36
-41
-8
-14
Sodus Bay embayment
Jul, 20.0
1.87
5.91
+82
+77
+20
+14
Sodus Bay nearshore
Jul, 20.0
0.29
0.81
-26
-28
-6
-10
Sandy Pond embayment
Jul, 20.0
1.11
6.06
+72
+67
+17
+12
Sandy Pond nearshore
Jul, 20.0
0.64
0.76
-21
-24
-4
-9
Frenchman's Bay (wet)
Jun, 22.5
8.94
1.57
+116
+104
+28
+21
Bronte Creek (wet)
Jun, 22.5
1.84
0.42
-14
-20
-3
-9
Location
Predicted % biomass gain or loss over 30 days
References
Lake Erie
west basin
central basin
east basin
Conroy et al., 2005 (NR)
Lougheed & ChowFraser, 2002, pers.
Comm..
Lake Ontario
35
Hall et al., 2003, pers.
comm. (NR)
Lougheed & ChowFraser ,2002, pers.
comm.
Cooke and Hill
Table 3, continued
Time of year,
water
temperature
(oC)
Phyto. wet
mass
(mg L-1)
Zoop. wet
mass
(mg L-1)
10 g BC
10 g SC
2400 g BC
2400 g SC
Chester
Aug, 22.0
5.00
0.05
+30
+23
+8
+2
Grand Tower
Oct, 16.0
10.0
0.01
+130
+130
+30
+26
Sum, 27.0
8.77
3.75
+127
+102
+31
+19
A. P. Levchuk unpubl.
+2
K. D. M. Dickerson
unpubl. (zoop);
Bukaveckas pers. comm.
(Chl a)
Location
Predicted % biomass gain or loss over 30 days
References
Middle Mississippi River
Upper Mississippi River
Missouri River
Sum, 23.0
5.30
1.39
+37
36
+27
+9
Williamson & Garvey
2005 (NR)
Cooke and Hill
Table 4. Maximum distance that can be travelled, based on bioenergetics models, by juvenile (10 cm, 10 g) and adult (60 cm, 2400 g)
bighead carp (BC) and silver carp (SC) over 30 days in different habitats at different times of year (Spr = spring; Sum = summer).
Open water habitats near wetlands are indicated after the site name (wet).
37
Cooke and Hill
Time of year;
water temp.
(oC)
10 g BC
10 g SC
2400 g BC
2400 g SC
Apr 1999
27.0
29.8
28.8
22.8
Jun 1999
33.4
31.4
35.0
24.4
Chippewa Park (wet)
Jul 1998
5.7
2.1
7.5
---
Pine Bay (wet)
Jul 1998
1.6
---
2.4
---
Hurkett Cove (wet)
Jul 1998
1.0
---
2.9
---
Collingwood Harbour
Jul 1998
0.8
---
2.6
---
west basin
Spr, 6.1
26.7
33.7
28.5
26.7
Sum, 22.0
13.2
10.6
14.8
3.9
central basin
Spr, 6.1
6.5
14.5
8.8
8.0
east basin
Spr, 6.1
10.9
18.7
13.0
12.2
Sodus Bay embayment
Jul 1997
26.7
25.1
28.5
18.1
Sandy Pond embayment
Jul 1997
24.9
23.1
26.4
16.3
Frenchman's Bay (wet)
Jun 1998
30.8
27.2
32.7
20.5
Middle MS River (Chester)
Aug, 22.0
12.4
9.3
14.3
2.6
Middle MS River (Grand Tower)
Oct, 16.0
38.1
38.9
39.9
32.1
Upper MS River
Sum, 27.0
29.3
22.8
31.1
15.6
Missouri River
Sum, 23.0
14.0
10.1
15.8
3.4
Location
Lake Michigan
Green Bay
Maximum distance of travel over 30 days (km)
Lake Superior
Lake Huron
Lake Erie
Lake Ontario
Rivers
38
Fig. 1. Specific energy consumption rates required for basic metabolic maintenance (no growth)
of (a) silver carp and (b) bighead carp. Values were generated for a 10 g juvenile (triangle), 70 g
sub-adult (square), 2400 g non-reproducing adult (open circle), and 2400 g female spawning 5%
of its body mass (closed circle) at swimming speeds of 0, 1, 2, 3 and 4 cm s-1.
Fig. 2. Estimated environmental energy density, Chl a concentration, and zooplankton dry mass
required for (a) silver carp and (b) bighead carp to meet basic metabolic demands (no growth).
Estimates were generated for a 10 g juvenile, 70 g sub-adult, 2400 g non-reproducing (NR) adult,
and 2400 g female spawning 5% of its body mass (R) at swimming speeds of 0 (black bars), 1
(gray bars) and 2 cm s-1 (white bars).
Fig. 3. Percent growth of 10 g silver carp (open triangles), 10 g bighead carp (closed triangles),
2400 g silver carp (open circles), and 2400 g bighead carp (closed circles), modelled over 30
days at a constant temperature. The model was run for multiple temperatures in each site and
month/ season. The plankton data for these sites and seasons are in Table 3.
Cooke and Hill
Fig. 1
specific consumption rate for metabolic maintenance (J g-1 d-1)
10000
1000
100
10
(a)
1
0
1
2
3
4
5
10000
1000
100
10
(b)
1
0
1
2
3
4
swimming speed (cm s-1)
swimming speed (cm s-1)
40
5
Cooke and Hill
0.04
50
0.02
0.000
required zooplankton biomass (g L-1)
100
0.06
0 cm/s
1 cm/s
2 cm/s
4000
150
3000
100
2000
50
1000
0
10 g
10 g
70 g
1070g g
0
2400
2400
g (R) 2400 g (R)
70 g (NR)
2400
g (NR)
2400 g (NR)
2400 g (R)
5000
4000
3000
2000
1000
0
body size and reproductive stage
0.10
150
0.08
100
0.06
0.04
50
0.02
0
0.00
5000
(b)
4000
200
0 cm/s
1 cm/s
2 cm/s
150
3000
100
2000
50
1000
0
10 g
10 g
70 g
1070g g
0
2400
2400
g (R) 2400 g (R)
70 g (NR)
2400
g (NR)
2400 g (NR)
body size and reproductive stage
2400 g (R)
body sizebody
and reproductive
stage
size and reproductive
stage
41
5000
4000
3000
2000
1000
0
required zooplankton biomass (mg L-1)
0.12
200
required zooplankton biomass (g L-1)
body size and reproductive stage
required chlorophyll a concentration (mg L-1)
required
density
required
energy
densityenergy
(kJ(mg
L-1
required
a concentration
chlorophyll
L)-1)
0.10
150
0.08
200
5000
(a)
required zooplankton biomass (mg L-1)
200
0.12
required chlorophyll a concentration (mg L-1)
-1
)
to maintainrequired
body chlorophyll
mass
(kJaLconcentration
required
))
energy
density
(kJ(mg
L-1L-1
Fig. 2
Cooke and Hill
percent growth
Fig. 3
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
50
Lake Michigan,
southern basin, Sep.
Lake Huron,
Collingwood
Harbour, Jul.
40
30
20
10
0
-10
-20
0
10
20
30
30
Lake Erie, central
basin, summer
20
10
0
-10
-20
-30
-40
0
10
20
30
0
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
20
30
Lake Ontario,
nearshore Sandy
Pond, Jul.
0
temperature (oC)
42
10
10
20
30